{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2D数据类别划分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**任务:**\n",
    "\n",
    "1、采用Kmeans算法实现2D数据自动聚类，预测V1=80,V2=60数据类别；\n",
    "\n",
    "2、计算预测准确率，完成结果矫正\n",
    "\n",
    "3、采用KNN、Meanshift算法，重复步骤1-2\n",
    "\n",
    "数据：data.csv"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "@Author  : Flare Zhao\n",
    "@Email: 454209979@qq.com\n",
    "@QQ讨论群：530533630  申请加群的验证信息为订单号（粘贴号码数字即可）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.072345</td>\n",
       "      <td>-3.241693</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.936710</td>\n",
       "      <td>15.784810</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.083576</td>\n",
       "      <td>7.319176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.120670</td>\n",
       "      <td>14.406780</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.711550</td>\n",
       "      <td>2.557729</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          V1         V2  labels\n",
       "0   2.072345  -3.241693       0\n",
       "1  17.936710  15.784810       0\n",
       "2   1.083576   7.319176       0\n",
       "3  11.120670  14.406780       0\n",
       "4  23.711550   2.557729       0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: labels, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define X and y\n",
    "X = data.drop(['labels'],axis=1)\n",
    "y = data.loc[:,'labels']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1156\n",
       "1     954\n",
       "0     890\n",
       "Name: labels, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "fig1 = plt.figure()\n",
    "plt.scatter(X.loc[:,'V1'],X.loc[:,'V2'])\n",
    "plt.title(\"un-labled data\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure()\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y==0],X.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y==1],X.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y==2],X.loc[:,'V2'][y==2])\n",
    "\n",
    "plt.title(\"labled data\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3000, 2) (3000,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=0, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set the model\n",
    "from sklearn.cluster import KMeans\n",
    "KM = KMeans(n_clusters=3,random_state=0)\n",
    "KM.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "centers = KM.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure()\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y==0],X.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y==1],X.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y==2],X.loc[:,'V2'][y==2])\n",
    "\n",
    "plt.title(\"labled data\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "#test data: V1=80,V2=60\n",
    "y_predict_test = KM.predict([[80,60]])\n",
    "print(y_predict_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    1149\n",
      "0     952\n",
      "2     899\n",
      "dtype: int64 2    1156\n",
      "1     954\n",
      "0     890\n",
      "Name: labels, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#predict based on training data\n",
    "y_predict = KM.predict(X)\n",
    "print(pd.value_counts(y_predict),pd.value_counts(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0023333333333333335\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y,y_predict)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the data and results\n",
    "fig4 = plt.subplot(121)\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y_predict==0],X.loc[:,'V2'][y_predict==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y_predict==1],X.loc[:,'V2'][y_predict==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y_predict==2],X.loc[:,'V2'][y_predict==2])\n",
    "\n",
    "plt.title(\"predicted data\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "\n",
    "fig5 = plt.subplot(122)\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y==0],X.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y==1],X.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y==2],X.loc[:,'V2'][y==2])\n",
    "\n",
    "plt.title(\"labled data\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    1149\n",
      "1     952\n",
      "0     899\n",
      "dtype: int64 2    1156\n",
      "1     954\n",
      "0     890\n",
      "Name: labels, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#correct the results\n",
    "y_corrected = []\n",
    "for i in y_predict:\n",
    "    if i==0:\n",
    "        y_corrected.append(1)\n",
    "    elif i==1:\n",
    "        y_corrected.append(2)\n",
    "    else:\n",
    "        y_corrected.append(0)\n",
    "print(pd.value_counts(y_corrected),pd.value_counts(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.997\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y,y_corrected))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "y_corrected = np.array(y_corrected)\n",
    "print(type(y_corrected))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig6 = plt.subplot(121)\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y_corrected==0],X.loc[:,'V2'][y_corrected==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y_corrected==1],X.loc[:,'V2'][y_corrected==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y_corrected==2],X.loc[:,'V2'][y_corrected==2])\n",
    "\n",
    "plt.title(\"corrected data\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "\n",
    "fig7 = plt.subplot(122)\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y==0],X.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y==1],X.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y==2],X.loc[:,'V2'][y==2])\n",
    "\n",
    "plt.title(\"labled data\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#establish a KNN model\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KNN = KNeighborsClassifier(n_neighbors=3)\n",
    "KNN.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n",
      "knn accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "#predict based on the test data V1=80, V2=60\n",
    "y_predict_knn_test = KNN.predict([[80,60]])\n",
    "y_predict_knn = KNN.predict(X)\n",
    "print(y_predict_knn_test)\n",
    "print('knn accuracy:',accuracy_score(y,y_predict_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    1156\n",
      "1     954\n",
      "0     890\n",
      "dtype: int64 2    1156\n",
      "1     954\n",
      "0     890\n",
      "Name: labels, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(pd.value_counts(y_predict_knn),pd.value_counts(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig6 = plt.subplot(121)\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y_predict_knn==0],X.loc[:,'V2'][y_predict_knn==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y_predict_knn==1],X.loc[:,'V2'][y_predict_knn==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y_predict_knn==2],X.loc[:,'V2'][y_predict_knn==2])\n",
    "\n",
    "plt.title(\"knn results\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "\n",
    "fig7 = plt.subplot(122)\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y==0],X.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y==1],X.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y==2],X.loc[:,'V2'][y==2])\n",
    "\n",
    "plt.title(\"labled data\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.84663454820215\n"
     ]
    }
   ],
   "source": [
    "#try meanshift model\n",
    "from sklearn.cluster import MeanShift,estimate_bandwidth\n",
    "#obtain the bandwidth\n",
    "bw = estimate_bandwidth(X,n_samples=500)\n",
    "print(bw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeanShift(bandwidth=30.84663454820215, bin_seeding=False, cluster_all=True,\n",
       "          max_iter=300, min_bin_freq=1, n_jobs=None, seeds=None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#establish the meanshift model-un-supervised model\n",
    "ms = MeanShift(bandwidth=bw)\n",
    "ms.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1149\n",
      "1     952\n",
      "2     899\n",
      "dtype: int64 2    1156\n",
      "1     954\n",
      "0     890\n",
      "Name: labels, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "y_predict_ms = ms.predict(X)\n",
    "print(pd.value_counts(y_predict_ms),pd.value_counts(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig6 = plt.subplot(121)\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y_predict_ms==0],X.loc[:,'V2'][y_predict_ms==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y_predict_ms==1],X.loc[:,'V2'][y_predict_ms==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y_predict_ms==2],X.loc[:,'V2'][y_predict_ms==2])\n",
    "\n",
    "plt.title(\"ms results\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "\n",
    "fig7 = plt.subplot(122)\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y==0],X.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y==1],X.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y==2],X.loc[:,'V2'][y==2])\n",
    "\n",
    "plt.title(\"labled data\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    1149\n",
      "1     952\n",
      "0     899\n",
      "dtype: int64 2    1156\n",
      "1     954\n",
      "0     890\n",
      "Name: labels, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#correct the results\n",
    "y_corrected_ms = []\n",
    "for i in y_predict_ms:\n",
    "    if i==0:\n",
    "        y_corrected_ms.append(2)\n",
    "    elif i==1:\n",
    "        y_corrected_ms.append(1)\n",
    "    else:\n",
    "        y_corrected_ms.append(0)\n",
    "print(pd.value_counts(y_corrected_ms),pd.value_counts(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "#convert the results to numpy array\n",
    "y_corrected_ms = np.array(y_corrected_ms)\n",
    "print(type(y_corrected_ms))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig6 = plt.subplot(121)\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y_corrected_ms==0],X.loc[:,'V2'][y_corrected_ms==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y_corrected_ms==1],X.loc[:,'V2'][y_corrected_ms==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y_corrected_ms==2],X.loc[:,'V2'][y_corrected_ms==2])\n",
    "\n",
    "plt.title(\"ms corrected results\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "\n",
    "fig7 = plt.subplot(122)\n",
    "label0 = plt.scatter(X.loc[:,'V1'][y==0],X.loc[:,'V2'][y==0])\n",
    "label1 = plt.scatter(X.loc[:,'V1'][y==1],X.loc[:,'V2'][y==1])\n",
    "label2 = plt.scatter(X.loc[:,'V1'][y==2],X.loc[:,'V2'][y==2])\n",
    "\n",
    "plt.title(\"labled data\")\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.legend((label0,label1,label2),('label0','label1','label2'))\n",
    "plt.scatter(centers[:,0],centers[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "kmeans\\knn\\meanshift\n",
    "kmeans\\meanshift: un-supervised, training data: X;kmeans: category number; meanshift: calculate the bandwidth\n",
    "knn: supervised; training data: X\\y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
